Intestinal Dysbiosis Associated with Systemic Lupus Erythematosus

Arancha Hevia,a Christian Milani,b Patricia López,c Adriana Cuervo,d Silvia Arboleya,a Sabrina Duranti,b Francesca Turroni,b*Sonia González,d Ana Suárez,c Miguel Gueimonde,a Marco Ventura,b Borja Sánchez,a* Abelardo Margollesa

Instituto de Productos Lácteos de Asturias, Consejo Superior de Investigaciones Científicas, Villaviciosa, Asturias, Spaina; Laboratory of Probiogenomics, Department ofLife Sciences, University of Parma, Parma Italyb; Immunology Areac and Physiology Area,d Department of Functional Biology, University of Oviedo, Asturias, Spain

* Present address: Francesca Turroni, Alimentary Pharmabiotic Centre and Department of Microbiology, Bioscience Institute, National University of Ireland, Cork, Ireland; BorjaSánchez, Department of Analytical Chemistry and Food Science, Faculty of Food Science and Technology, University of Vigo, Ourense, Spain.

ABSTRACT Systemic lupus erythematosus (SLE) is the prototypical systemic autoimmune disease in humans and is characterizedby the presence of hyperactive immune cells and aberrant antibody responses to nuclear and cytoplasmic antigens, includingcharacteristic anti– double-stranded DNA antibodies. We performed a cross-sectional study in order to determine if an SLE-associated gut dysbiosis exists in patients without active disease. A group of 20 SLE patients in remission, for which there wasstrict inclusion and exclusion criteria, was recruited, and we used an optimized Ion Torrent 16S rRNA gene-based analysis pro-tocol to decipher the fecal microbial profiles of these patients and compare them with those of 20 age- and sex-matched healthycontrol subjects. We found diversity to be comparable based on Shannon’s index. However, we saw a significantly lower Firmic-utes/Bacteroidetes ratio in SLE individuals (median ratio, 1.97) than in healthy subjects (median ratio, 4.86; P < 0.002). A lowerFirmicutes/Bacteroidetes ratio in SLE individuals was corroborated by quantitative PCR analysis. Notably, a decrease of someFirmicutes families was also detected. This dysbiosis is reflected, based on in silico functional inference, in an overrepresentationof oxidative phosphorylation and glycan utilization pathways in SLE patient microbiota.

IMPORTANCE Growing evidence suggests that the gut microbiota might impact symptoms and progression of some autoimmunediseases. However, how and why this microbial community influences SLE remains to be elucidated. This is the first report de-scribing an SLE-associated intestinal dysbiosis, and it contributes to the understanding of the interplay between the intestinalmicrobiota and the host in autoimmune disorders.

Received 25 June 2014 Accepted 29 August 2014 Published 30 September 2014

Citation Hevia A, Milani C, López P, Cuervo A, Arboleya S, Duranti S, Turroni F, González S, Suárez A, Gueimonde M, Ventura M, Sánchez B, Margolles A. 2014. Intestinal dysbiosisassociated with systemic lupus erythematosus. mBio 5(5):e01548-14. doi:10.1128/mBio.01548-14.

Invited Editor Nicola Segata, University of Trento Editor Maria Gloria Dominguez Bello, New York University School of Medicine

Copyright © 2014 Hevia et al. This is an open-access article distributed under the terms of the Creative Commons Attribution-Noncommercial-ShareAlike 3.0 Unportedlicense, which permits unrestricted noncommercial use, distribution, and reproduction in any medium, provided the original author and source are credited.

Address correspondence to Borja Sánchez, borja.sanchez@uvigo.es, or Abelardo Margolles, amargolles@ipla.csic.es.

Metagenomic studies on gut microbiota burst onto the scien-tific scene during the last decade, due to the advent of next-

generation sequencing techniques. In a very short period of time,microbiologists moved from the study of single, isolated, cultiva-ble microorganisms, specifically, those able to grow under stan-dard laboratory conditions, to the investigation of very complexmicrobial communities, mainly composed of uncultivable bacte-ria (1, 2). The first metagenomics reports enabled an overview ofthe complexity of our gut microbial communities (3, 4). Furtherstudies focused on establishing the correlation between the hu-man gut microbiome, the collective genomes of all microbes in-habiting the gut (5), and different physiological states, includingthose having an influence on health. Currently, we know that thegut microbiota might affect food and drug metabolism (6), influ-ences human behavior (7), shifts during the course of pregnancy(8), displays age-associated changes (9–12), and possesses distinc-tive features depending on geographical location (12, 13), amongother features. It is also becoming clear that there is a strong linkbetween dietary patterns and the gut microbial profile (14, 15).Furthermore, some links have been established between some dis-orders (for example, obesity and metabolic syndrome) and an

imbalance in the gut microbial ecology, also called dysbiosis (16–18). Remarkably, intestinal dysbiosis has also been associated withautoimmune diseases, such as rheumatoid arthritis, type 1 diabe-tes, and inflammatory bowel disease (IBD) (19–21).

Systemic lupus erythematous (SLE) is a prototypical autoim-mune disease in humans that is characterized by the presence ofhyperactive immune cells and aberrant antibody responses to nu-clear and cytoplasmic antigens. Genetic, immunological, hor-monal, and environmental factors contribute to disease suscepti-bility (22), and its prevalence varies greatly depending on thepopulation under study, although a prevalence of 2 to 5 cases per10,000 inhabitants is reportedly considered normal (23). Amongthe environmental factors, growing evidence suggests that molec-ular mimicry as a result of viral infection may contribute to thedevelopment of lupus (24). Also, some reports have highlightedintestinal infections that may ameliorate SLE symptoms (25), anda marked difference in the specificity of antibodies to bacterialDNA in healthy people and SLE patients has been indicated (26).In fact, there is early evidence of a different abundance of cultiva-ble intestinal bacteria in SLE (27). Remarkably, it has been sug-gested that novel SLE biomarkers can be potentially found in the

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human microbiota (28). However, a study of the potential dysbio-sis associated with SLE had not been tackled until now. Therefore,in this report we took advantage of next-generation sequencingtechniques to explore the potential interplay of the human micro-biome and SLE. We have proven, for the first time, that there is agut microbial dysbiosis associated with SLE.

RESULTS AND DISCUSSION

Despite all the scientific knowledge generated in the last few years,and although few studies published so far support the dysbiosistheory as a key factor promoting chronic inflammation in auto-immune diseases (29–32), there is no scientific work that hastaken advantage of next-generation sequencing techniques to ex-plore the potential interplay of the human microbiome and SLE,the prototypical autoimmune disease in humans. Therefore, wedesigned our work with the aim of answering if there is an SLE-associated intestinal dysbiosis and, if so, which microbial popula-tion groups are related to the dysbiosis.

We defined the SLE population group by considering thatthere is a census of about 300 SLE patients in Asturias (from a totalpopulation of about 1,000,000 inhabitants). Thus, we were able toobtain a group of SLE patients from a well-defined geographicallocation to compare them with a similar group of healthy controls(HC), considering factors such as sex, age, medication (absence ofantibiotic, steroid, and immunological treatments during the last6 months), medical history (presenting a wide variety of clinicalSLE manifestations), duration of the disease (2 to 24 years), andabsence of flares of disease activity at the time of sampling (sys-temic lupus erythematosus disease activity index [SLEDAI] scoreof �8 at the time of sample collection). The group of SLE patientsincluded individuals with a large variety of symptoms (Table 1),allowing us to establish correlations between the microbial profile

and SLE, which are very likely independent of a specific pattern ofsymptoms. This variability in the phenotype of the disease is anintrinsic characteristic of SLE (22). We also selected patients withno active disease at the time of sampling, because the clinical man-ifestations of the disease in this population group are not biased bythe pharmacological treatment necessary to treat SLE individualsduring disease relapse. Furthermore, mean dietary intakes of en-ergy, macronutrients, micronutrients, fiber, and phyto-compounds were recorded, both from patients with SLE andhealthy subjects, and we found that there was no significant dif-ference between the 2 groups (Table 2). Also, no significant dif-ference was found between the 2 groups regarding lifestyle-relatedfactors (smoking, alcohol consumption, physical activity, and useof vitamin and mineral supplements [data not shown]). This re-duced the possibility that our analysis was affected by factorsshown to have an influence on the gut microbial profile, such asage (9), diet (15), or phenolic compound intake (33, 34).

Our work is based on 16S rRNA gene-based data for fecal mi-crobiota and the bioinformatic analysis of the results. In a previ-ous work (35), we optimized protocols to study the human fecalmicrobial population by using an Ion Torrent PGM sequencingplatform. This methodology was applied in the current study, andwe obtained an average of 592,305 high-quality reads per fecalsample (see Table S1 in the supplemental material). Rarefactioncurves obtained by plotting the Shannon, Chao1, and phyloge-netic diversity indexes against the number of sequences (seeFig. S1 in the supplemental material) showed that a large part ofthe diversity of the samples was detected. The microbiota compo-sition at the phylum and family levels was obtained (Fig. 1; see alsoFig. S2 in the supplemental material). Remarkably, even consid-ering the broad heterogeneity of the clinical manifestations of SLE

TABLE 1 Demographic, clinical, and immunological features of SLE patients

Subjectno.

Age(yrs)

Diseaseduration(yrs)

Anti-dsDNAtiter (U/ml)a

Complement C3(g/liter)a

Complement C4(g/liter)a Clinical and immunological featuresb

SLE1 43 2 0.3 0.93 0.2 MR, PH, HD, ANASLE2 68 3 0.7 0.96 0.18 MR, DL, PH, ARSLE4 35 4 7.7 1.53 0.22 PH, OU, RD, ANASLE5 50 5 18 1.67 0.44 PH, OU, HD, ANA, anti-SSaSLE6 35 3 48 0.81 0.13 MR, OU, AR, HD, ANA, anti-dsDNA, anti-SSaSLE7 70 3 27 1.74 0.37 PH, OU, HD, ANA, anti-dsDNASLE11 54 24 99.1 1.43 0.28 MR, DL, PH, AR, HD, ANA, anti-dsDNA, anti-SmSLE12 58 6 13 0.84 0.22 DL, PH, AR, HD, ANA, anti-SSa, RFSLE13 40 6 0.6 0.83 0.25 MR, OU, ANASLE14 40 12 4 0.92 0.18 AR, SE, RD, ANA, anti-SSbSLE15 51 24 104 0.83 0.14 MR, DL, PH, AR, SE, HD, ANA, anti-dsDNA, anti-SSa,

anti-SSb, anti-Sm, anti-RNP, anti-CLPSLE16 54 24 45 1.76 0.3 PH, AR, ANA, anti-dsDNA, anti-SSaSLE17 46 13 19 0.8 0.11 MR, DL, PH, OU, AR, ANA, anti-dsDNA, anti-SSa, RFSLE18 43 12 4.1 1.04 0.16 DL, PH, OU, AR, SE, HD, ANA, anti-SSa, RFSLE19 34 4 0 1.19 0.25 MR, PH, OU, ANASLE20 51 7 5.8 0.67 0.14 PH, OU, ANA, anti-SSaSLE21 59 11 1.2 1.16 0.17 PH, ANA, anti-dsDNA, anti-SSa, anti-CLPSLE22 64 11 4.4 1.17 0.25 MR, PH, AR, ANA, anti-dsDNA, anti-SSaSLE24 46 14 0.4 1.08 0.4 MR, PH, HD, ANA, anti-SSa, RFSLE26 46 20 38 0.89 0.18 MR, PH, OU, RD, HD, ANA, anti-dsDNA.a At the time of sampling.b Cumulatively registered. Abbreviations: ANA, antinuclear antibodies; anti-RNP, antiribonucleoprotein antibodies; anti-Sm, anti-Smith antigen antibodies; anti-CLP,anticardiolipin antibodies; RF, rheumatoid factor; AR, arthritis; DL, discoid lesions; HD, hematological disorder; MR, malar rash; OU, oral ulcers; PH, photosensitivity; RD, renaldisorder; SE, serositis.

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in the individuals under study (Table 1), we obtained a particulartype of microbiota for the SLE group. In this regard, the presenceof anti– double-stranded DNA (dsDNA) antibodies and otherclinical data were organized in a metadata file for all the microbi-ota profiles. A principal component analysis (PCoA) was per-formed with both metadata/microbiota profiles, using the vari-ability of the 16S rRNA gene profiling at different taxonomiclevels. Sample classification according to the metadata revealed nospecific clustering of the samples or correlations with the differentclinical features or anti-dsDNA antibodies (data not shown).

In silico analysis of the sequences highlighted the key findingsof our work. A high-quality filtering approach was used in order toprocess the Ion Torrent-generated sequencing data (see Table S1in the supplemental material); a total of 293,436 unfiltered oper-ational taxonomic units (OTUs) were identified by using uclustfor de novo OTU picking. Based on each of five alpha-diversitymeasures (Chao1, PD whole tree, observed species, Shannon, andSimpson indexes), patients and controls were not significantlydifferent (data not shown). Notably, one of the main results wasthe identification of a clear dysbiosis between the two studygroups which was characterized by a higher relative abundance of

TABLE 2 General characteristics and mean dietary intake of energy,macronutrients, fiber forms, and phyto-compounds in patients withSLE and healthy controls

CharacteristicSLE patients(n � 20)

Healthy controls(n � 20)

Female sex (%) 100 100Age (yrs) 49.2 � 10.7b 46.9 � 8.6BMI (kg/m2) 26.1 � 5.3 25.2 � 4.2Energy (kcal/day) 2,173.1 � 722.4 1,875.9 � 332.8Lipid (g/day)a 84.5 � 41.0 85.4 � 20.5MUFA (g/day)a 35.3 � 19.7 35.7 � 7.6PUFA (g/day)a 17.2 � 9.7 17.5 � 9.4SFA (g/day)a 24.9 � 14.1 25.0 � 6.0Protein (g/day)a 104.9 � 27.6 100.6 � 20.9Carbohydrates (g/day)a 205.0 � 75.6 203.5 � 47.0Dietary fiber (g/day)a 24.9 � 10.4 25.3 � 9.1Insoluble fiber (g/day)a 16.0 � 8.6 16.6 � 7.5Soluble fiber (g/day)a 2.9 � 1.5 2.8 � 1.1Total isoflavones (mg/day)a 2.4 � 2.4 2.5 � 2.7Total phenolics (mg/day)a 833.2 � 527.3 916.3 � 437.8a Model was adjusted for energy and BMI. PUFA, polyunsaturated fatty acids; MUFA,monounsaturated fatty acids; SFA, saturated fatty acids.b Values are means � SD.

FIG 1 Aggregate microbiota composition in fecal samples from control (HC) and lupus-affected (SLE) subjects at the phylum level (a) and family level (b). Inpanel b, only taxonomic groups representing �0.5% are shown.

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Bacteroidetes in the SLE group. Overall, we detected a significantdecrease in the Firmicutes/Bacteroidetes ratio in SLE individuals:the microbiota of SLE patients, compared with controls, had analmost-2.5-fold-decreased ratio (Fig. 2A) (P � 0.002). Looking atthe different individuals, a gradient from lower (SLE) to higher(HC) Firmicutes/Bacteroidetes ratios was observed (Fig. 2B). These16S rRNA gene-based analyses were corroborated by quantitativePCR (qPCR) analysis. The levels (reported as the log cells/g, withthe interquartile range [IQR] in parentheses) of total fecal bacteriawere 10.62 (9.46 to 10.80) in SLE patients and 10.35 (10.07 to10.59) in controls. Firmicutes levels in the SLE and control groupsreached 9.69 (8.86 to 10.38) and 9.99 (9.68 to 10.31), respectively,while those of Bacteroidetes were 10.52 (9.56 to 10.83) and 9.89(9.59 to 10.23), respectively. No statistically significant differencesin these levels of microbial groups were found between SLE pa-tients and controls. However, when the data were expressed as therelative percentages of Firmicutes and Bacteroidetes with respect tothe total bacterial level, a significantly higher (P � 0.05) percent-age of Bacteroidetes was observed in the SLE group. Moreover,when the Firmicutes/Bacteroidetes ratio was calculated, a statisti-

cally significant (P � 0.01) decrease in the SLE group with respectto the control group was found (0.94 [0.90 to 0.98] versus 1.01[0.96 to 1.06], respectively). The differences in the ratios based onqPCR were less pronounced than the differences obtained withthe 16S rRNA profiling, likely because the two techniques providedifferent kinds of information: a relative proportion of sequences(from the 16S rRNA gene-based analysis) versus an absolutequantification of sequences (via qPCR). Thus, the fact that weobtained clear evidence for a significantly lower Firmicutes/Bacte-roidetes ratio in SLE patients when we used two different culture-independent techniques supports the soundness and reliability ofour analyses. The phyla Bacteroidetes and Firmicutes include themost abundant components of the human gut microbiota (36).Dysbiosis between Firmicutes and Bacteroidetes in the human guthas been described in previous studies in association with somedisorders. The ratio between Firmicutes and Bacteroidetes de-creases in human type 2 diabetes compared with controls (37).Also, most studies of the microbiota in people with Crohn’s dis-ease report a decrease in the abundance of Firmicutes and an in-crease in Bacteroidetes in association with the disease (38). An

FIG 2 (A) Box plot of Firmicutes/Bacteroidetes ratios (median � IQR) in SLE patients versus healthy controls. (B) Percentages of 16S rRNA reads of Bacteroidetes(red bars) and Firmicutes (blue bars) in the DNA extracted from fecal samples of SLE patients (SLE codes) and healthy controls (HC codes). Ratios aresignificantly different (P � 0.002).

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opposite situation is observed in obesity, in which the dysbiosis ischaracterized by an increase in the Firmicutes/Bacteroidetes ratio(17). Therefore, this specific microbial balance between the moreabundant phyla in the human gut seems to be dependent on thephysiology of the disorder. In relation to this, it has been reportedthat this equilibrium is susceptible to modification by shifts in thedietary pattern. Wu et al. (15) reported that Firmicutes levels werepositively associated with a low-fat/high-fiber diet. Also, dietaryinterventions, including whole grain in the diet, increase the Fir-micutes/Bacteroidetes ratio (39).

We prepared scatter plots (see Fig. S3A in the supplementalmaterial), and they clearly highlighted a positive association be-tween Firmicutes in healthy controls (P � 0.01) and Bacteroidetesin SLE patients (P � 0.001). This association was confirmed atlower taxonomic levels, and normalized abundances of the classesBacteroidia (phylum Bacteroidetes) and Clostridia (phylum Firmi-cutes) and the orders Bacteroidales and Clostridiales differed sig-nificantly between the SLE patients and the healthy controls (seeFig. S3B). At the family level, Lachnospiraceae (P � 0.05) andRuminococcaceae (P � 0.05) were positively associated withhealthy controls (see Fig. S4 and S5 and Table S2 in the supple-mental material).

Statistical differences between the two groups (HC and SLE)were calculated by a PERMANOVA test, with the distance dataobtained after ordination analysis (PCoA) using PAST v 3.1 (40).In all cases, data from the relative taxa abundances were used, anddistances were computed according to the Bray-Curtis similarityindex. The groups of SLE and HC differed statistically wheneverphylum- or family-level data were used (P � 0.01 or P � 0.02,respectively).

Unsupervised PCoA of the 16S rRNA sequence data identifiedthe phyla Bacteroidetes and Firmicutes as the main gradients forSLE and healthy control groups, respectively (Fig. 3A). PCoA atthe family level showed that the families Lachnospiraceae and Ru-minococcaceae were located near the healthy controls (Fig. 3B).Interestingly, Lachnospiraceae was the most abundant family inthe feces of both study groups (Fig. 1; see also Fig. S2 in the sup-plemental material). Depletion of Lachnospiraceae and Rumino-coccaceae has been associated with Clostridium difficile infectionsand nosocomial diarrhea (41). Also, several studies showed a de-crease of Lachnospiraceae in IBD patients, and this family has beensuggested as a biomarker of disease activity (42–44). However, thefunctional consequences of the depletion of these bacteria in thepreviously mentioned intestinal diseases remain to be investi-gated. Although the amplicons of the 16S rRNA sequences couldbe relatively short to perform a totally reliable population struc-ture analysis at the genus/species level, it is noteworthy that therelative abundance of sequences assigned to Bacteroides spp. weresignificantly higher in SLE samples (P � 0.02). In the controlgroup, we also found a significantly higher (P � 0.05) relativeabundance of sequences tentatively assigned to Desulfovibrio, themost common genus of sulfate-reducing bacteria in the humangut (45). It is worth mentioning that some authors have describedhow dysbiosis could affect mucosal barrier function and impairimmunoregulatory mechanisms, leading to pathological effects insystemic immunity. Using animal models, a direct involvement ofcomponents of the microbiota in chronic intestinal inflammation(46) and the protective role of specific commensals in avoidingbacterial translocation (47) have been demonstrated. Finally, weshould bear in mind that nonsteroidal anti-inflammatory drugs

and the antimalarial treatment of SLE patients could have an in-fluence on the observed dysbiosis.

In our study, we also determined the tentative metagenomesfrom phylogenetically associated reference genomes. Our aim wasto highlight metabolic pathways and shifts associated with the SLEpopulation compared with controls. We inferred the functionalityof the different putative metagenomes by using PICRUSt soft-ware, which allows the prediction of metabolic pathways from the16S rRNA reads (48). A functional analysis using the data ob-tained from the KEGG pathways at level 3 allowed us to detectcertain processes potentially associated with either healthy con-trols or SLE. KEGG levels are the different hierarchical subdivi-sions in which the functions of a biological system (cell, organism,or ecosystem) are arranged according to information organized inthe KEGG database (http://www.genome.jp/kegg/kegg1a.html).Pathways displaying a difference in mean proportions betweenhealthy controls and SLE groups of at least 0.1% are represented inFig. S6 in the supplemental material. Some glycan degradationpathways are slightly overrepresented in the microbiota of SLEpatients, probably due to the higher abundance of Bacteroidetes inthese samples. Bacteroidetes, and specifically the main genus of thisphylum, Bacteroides, have been shown to display broad glycan-degrading abilities (49). The same occurs with lipopolysaccharidebiosynthesis proteins, which is in direct relation to the higherabundance of Bacteroidetes in the SLE samples. Remarkably, oxi-dative phosphorylation processes seem to be associated with SLEpatients. This finding indicates that some bacteria able to performoxidative phosphorylation may be better adapted to the intestinalecosystem of individuals with SLE, and this could be related to theimbalance in the oxidative stress environment at the intestinallevel that has been linked to some autoimmune diseases (48).

In summary, understanding and potentially manipulating im-mune responses through the action of intestinal microbiota com-prise one of the most active fields in probiotic and prebiotic re-search. Most likely, the dysbiosis defined in this work is theconsequence of the altered immune function of SLE patients. Atpresent, the treatment of SLE patients is exclusively performedwith drugs, and our findings could indicate that challenging theimmune system with the bacteria depleted in SLE, or substancespromoting their growth, could influence SLE physiology. To thebest of our knowledge, experimental papers about the relationshipof the human gut microbiome and SLE have not been published,and this is the first report describing an SLE-associated intestinaldysbiosis. Thus, our results establish the basis to delve deeper intothe understanding of the relationship between the human gut mi-crobiota and autoimmune diseases.

MATERIALS AND METHODSEthics statement. Ethics approval for this study (reference codeAGL2010-14952; grant title “Towards a better understanding of gut mi-crobiota functionality in some immune disorders”) was obtained fromthe Bioethics Committee of CSIC (Consejo Superior de InvestigacionesCientíficas) and from the Regional Ethics Committee for Clinical Re-search (Servicio de Salud del Principado de Asturias) in compliance withthe Declaration of Helsinki. All determinations were performed with fullyinformed written consent from all participants involved in the study.

Study subjects. The study sample comprised 20 patients with SLE(SLE codes) and 20 healthy controls (HC codes). SLE patients were se-lected from the updated Asturian Register of Lupus (Asociación Lúpicosde Asturias, Oviedo, Spain). All of them fulfilled at least four of the Amer-ican College of Rheumatology criteria for SLE (50). The individuals re-

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FIG 3 PCoA results for the 16S rRNA profiles at the phylum (A) and family (B) level. The presence or absence of SLE was further included as metadata. HC,closed circles; SLE, open triangles.

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cruited for the SLE group were in remission and had not had any immu-notherapy or corticoid treatment during the previous months, since thoseare the treatments that could have the strongest influence on the physiol-ogy of the patients. All patients were women of Caucasian origin, 49.2 �10.7 years old (mean � standard deviation [SD]), and had no active dis-ease at the time of sampling (SLEDAI score of �8). Information on clin-ical manifestations was obtained by reviewing clinical records (Table 1).Patients were also asked precise questions regarding treatment receivedduring the previous 6 months. Only those individuals who had not usedantibiotics, glucocorticoids, immunosuppressive drugs, monoclonal an-tibodies, or other immunotherapies were recruited for the study. Eighteenpatients were receiving antimalarial treatment, and all of them were reg-ular consumers of nonsteroidal anti-inflammatory drugs. Twenty age-matched healthy women (46.9 � 8.6 years old) from the same populationwere recruited as controls.

(i) Nutritional assessment. Variables of macro- and micronutrientintakes were collected by means of a semiquantitative food frequencyquestionnaire that included 160 items. During a personal interview, car-ried out within 7 days after the collection of the fecal sample, subjects wereasked item by item whether they usually ate the food and, if so, how muchthey ate. For this purpose, three different serving sizes of each cooked foodwere presented in pictures to the participants so that they could choosefrom up to 7 serving sizes (from “less than the small one” to “more thanthe large one”). For some of the foods consumed, amounts were recordedin household units, by volume, or by measuring with a ruler. Specialattention was paid to cooking practices and number and amount of in-gredients used in each recipe, as well as questions concerning menu prep-aration (e.g., type of oil or milk used).

Food intake was analyzed for energy and macro- and micronutrientcontents by using the nutrient food composition tables developed byCESNID (51). Total, soluble, and insoluble fiber intake was completedbased on Marlett food composition tables (52), and polyphenol contentwas calculated from the U.S. Department of Agriculture (USDA) NutrientDatabase (53).

(ii) Anthropometric measures. The body mass index (BMI) was cal-culated from the following formula: weight/(height)2 (in kg/m2). Heightwas registered by using a stadiometer with an accuracy of �1 mm (Año-Sayol, Barcelona, Spain). Subjects were barefoot, in an upright position,and with the head positioned in the Frankfort horizontal plane. Weightwas measured on a scale with an accuracy of �100 g (Seca, Hamburg,Germany).

(iii) Lifestyle-related factors. During the interview, other factors as-sociated with the lifestyle of the subject were registered. Smoking habit,physical activity, alcohol consumption, and supplements use were in-cluded in the questionnaire. Smoking status was categorized as non-smoker (including exsmokers and occasional smokers) or currentsmoker. Those subjects who reported that they never exercised were cat-egorized as physically inactive. “Regular alcohol consumer” refers to thosesubjects who declared a regular consumption of alcoholic drinks. Also, theuse of vitamin and mineral supplements during the last month was self-reported.

Fecal sample collection and DNA extraction. Fresh fecal material(between 10 and 50 g per person) was collected in a sterile container andimmediately manipulated and homogenized within a maximum of 3 hfrom defecation. During the waiting period, from defecation to homoge-nization, samples were kept at 4°C. Thirty milliliters of RNAlater solution(Applied Biosystems, Foster City, CA) was added to 10 g of sample, andthe mixture was homogenized in a sterile bag, using a stomacher appara-tus (IUL Instruments, Barcelona, Spain) with three cycles at high speed,1 min per cycle. Homogenized samples were then stored at �80°C untiluse.

For DNA extraction, samples were thawed and the QIAamp DNAstool minikit (Qiagen Ltd., Strasse, Germany) was used as previously de-scribed (35).

16S rRNA gene amplification. Partial 16S rRNA gene sequences wereamplified from extracted DNA by using the primer pair Probio_Uni (5=-CCTACGGGRSGCAGCAG-3’)/Probio_Rev (5=-ATTACCGCGGCTGCT-3=) (35), which targets the V3 region of the 16S rRNA gene sequence.The PCR conditions used were 5 min at 95°C, 35 cycles of 30 s at 94°C, 30 sat 55°C, and 90 s at 72°C, followed by 10 min at 72°C. Amplification wascarried out using a Verity thermocycler (Applied Biosystems). The integ-rity of the PCR amplicons was analyzed by electrophoresis on an Experionworkstation (Bio-Rad, Hertfordshire, United Kingdom).

Ion Torrent PGM sequencing of 16S rRNA gene-based amplicons.The PCR products derived from amplification of specific 16S rRNA genehypervariable regions were purified by electrophoretic separation on a1.5% agarose gel and the use of a Wizard SV Gen PCR cleanup system(Promega, Madison, WI), followed by a further purification step involv-ing Agencourt AMPure XP DNA purification beads (Beckman CoulterGenomics GmbH, Bernried, Germany) in order to remove primer dimers.The DNA concentration of the amplified sequence library was estimatedthrough use of the Experion system (Bio-Rad). From the concentrationand the average size of each amplicon library, the amount of DNA frag-ments per microliter was calculated and libraries for each run were dilutedto 3E9 DNA molecules prior to clonal amplification. Emulsion PCR wascarried out using the Ion OneTouch 200 template kit v2 DL (Life Tech-nologies, Guilford, CA) according to the manufacturer’s instructions. Se-quencing of the amplicon libraries was carried out on 316 chips by usingthe Ion Torrent PGM system and employing the Ion Sequencing 200 kit(Life Technologies) according to the supplier’s instructions at the DNAsequencing facility, GenProbio s.r.l. After sequencing, the individual se-quence reads were filtered with the PGM software to remove low-qualityand polyclonal sequences. Sequences matching the PGM 3= adaptor werealso automatically trimmed. All PGM quality-approved, trimmed, andfiltered data were exported as SFF files.

Sequence-based microbiota analysis. The SFF files were processedusing QIIME (54). Quality control retained sequences had lengths be-tween 150 and 200 bp, a mean sequence quality score of �25, and withtruncation of a sequence at the first base if a low-quality rolling 10-bpwindow was found. Presence of homopolymers of �7 bp and sequenceswith mismatched primers were omitted. In order to calculate downstreamdiversity measures (alpha and beta diversity indices, Unifrac analysis), 16SrRNA OTUs were defined at �97% sequence homology by using uclust(55). Chimeric sequences were removed using ChimeraSlayer (56). Fur-thermore, OTUs that included less than 10 sequences were filtered usingQIIME (54). All reads were classified to the lowest possible taxonomicrank by using QIIME and a reference data set from the Ribosomal Data-base Project (57). The sequence data features of all the samples are in-cluded in Table S1 in the supplemental material.

Different alpha diversity indexes were calculated using QIIME andinformation from the OTU tables using the alpha_diversity.py script. Thedifferent diversity metrics were set by passing the option –s to the script.The following indexes were calculated for every sample and comparedbetween groups by using a two-sided Student’s t test: Chao1, PD wholetree, observed species, Shannon, and Simpson.

Analysis by qPCR. Quantification of total fecal bacteria, Firmicutes,and Bacteroidetes by qPCR was performed by using previously describedprimers and conditions (58–60). Analyses were done in duplicate in a7500 Fast real-time PCR system (Applied Biosystems) using Sybr greenPCR master mix (Applied Biosystems). Standard curves were made withpure cultures, grown under anaerobic conditions at 37°C, of Escherichiacoli LMG 2092 in Gifu anaerobic medium (GAM; Nissui PharmaceuticalCo., Tokyo, Japan), Faecalibacterium prausnitzii DMSZ 17677 in RCMformula (Oxoid Ltd., Basingstoke, Hampshire, United Kingdom), andBacteroides thetaiotaomicron DSMZ 2079 in GAM.

Functional inference. The functionality of the different metag-enomes, grouped by disease status (healthy control versus SLE), was pre-dicted using the software PICRUSt 0.9.1 (http://picrust.github.io), whichhas been explained in detail elsewhere (48). In short, this software allows

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the prediction of functional pathways from the 16S rRNA reads. First, acollection of closed-reference OTUs was obtained from the filtered readsby using QIIME v 1.7.0 (54) and by querying the data against the IMG/GGreference collection (GreenGenes database, May 2013 version; http://greengenes.lbl.gov). Reverse-strand matching was enabled during thequery, and OTUs were picked at a 97% identity. A BIOM-formatted table(biological observation matrix [61]) was obtained with the pick_closed_reference_otus.py script. This table, containing the relative abundances ofthe different reference OTUs in all the metagenomes, was normalizedbased on the predicted 16S rRNA copy number by using the scriptnormalize_by_copy_number.py. Final functional predictions, in-ferred from the metagenomes, were created with the script predict_metagenomes.py. When necessary, tab-delimited tables were obtainedwith the script convert_biom.py.

Analysis of predicted metagenomes. PICRUSt and QIIME provide anumber of scripts that can be useful for analyzing both 16S rRNA generelative abundances and the predicted metabolic data. Predicted metag-enomic contents were collapsed at KEGG pathway level 3 (http://www.genome.jp/kegg/pathway.html) with the categorize_by_function.py script, and the data were analyzed statistically by using STAMP v 2.0.0(62). STAMP allows data filtering and the application of different statis-tical tests and corrections, including PCoA. It also generates differentgraphics, including box plots, error plots, and scatter plots. Data ofthe KEGG pathway distributions were plotted by using the scriptsummarize_taxa_through_plots.py. Associations of different taxonomiccategories to SLE were statistically analyzed with the script otu_category_significance.py.

Statistical analyses. Statistical analysis was performed using IBM-SPSS version 19.0 (IBM SPSS, Inc., Chicago, IL). For descriptive purposes,in Table 2 the mean values are presented as means � SD on untrans-formed variables. Differences between SLE patients and controls werecompared by using a multivariate linear model, including energy intakeand BMI as covariates.

Individuals were ordered according to their sequence data composi-tion by principal component analysis using the taxonomic data at thephylum and family levels. Patterns were extracted using all the variationsfrom the taxonomic data via an indirect method as a model and SLE asmetadata. To analyze the associations between inferred metabolic path-ways and SLE, metabolic pathways with very low abundance levels(�0.001 in 50% of the samples) were excluded from all analyses. Associ-ation of KEGG pathways to SLE were identified by running two-sidedWelch tests on every pair of means. This test is a variation of Student’st test and is used when equal variance cannot be assumed in both groups.Confidence intervals (95%) were obtained by inverting the Welch’s tests.The false discovery rate (FDR) correction (48, 63) was finally applied in allcases, and significant differences between healthy controls and SLE pa-tients were only considered when below a P value of 0.05 or a q valuebelow 0.2. P and q values at the phylum and family levels are included inTable S2 in the supplemental material. In the particular case of the familyDesulfovibrionaceae (with P � 0.05), further statistical analysis was carriedout for sequences tentatively assigned to the genus Desulfovibirio by usinga one-sided t test.

In relation to qPCR results, not all the bacterial groups showed normaldistribution; therefore, differences in bacterial levels between groups ofindividuals were analyzed using a nonparametric test (Mann-WhitneyU test).

Nucleotide sequence accession number. The raw sequences reportedin this article have been deposited in the NCBI Short Read Archive (SRA;study accession number SRP028162).

SUPPLEMENTAL MATERIALSupplemental material for this article may be found at http://mbio.asm.org/lookup/suppl/doi:10.1128/mBio.01548-14/-/DCSupplemental.

Figure S1, DOC file, 0.2 MB.Figure S2, DOC file, 0.1 MB.Figure S3, DOC file, 0.3 MB.

Figure S4, DOC file, 0.2 MB.Figure S5, DOC file, 0.5 MB.Figure S6, DOCX file, 0.2 MB.Table S1, PDF file, 0.04 MB.Table S2, PDF file, 0.4 MB.

ACKNOWLEDGMENTS

This study was financed by European Union FEDER funds and the Span-ish Plan Nacional de I�D (grant AGL2010-14952). Arancha Hevia wasthe recipient of an FPI grant, and Borja Sánchez was the recipient of a Juande la Cierva postdoctoral contract, both from the Spanish Ministerio deCiencia e Innovación.

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